Semantic Segmentation of Node and Edge Diagrams for Assistive Technology
Quick Answer
This paper introduces novel deep learning models for semantic segmentation of node-link diagrams, achieving over 93% per-pixel accuracy on a large synthetic dataset.
Quick Take
These models enhance accessibility for assistive technologies, addressing the challenge of interpreting bitmap representations of complex diagrams.
Key Points
- Models achieve over 93% accuracy in semantic segmentation of node-link diagrams.
- Focus on improving accessibility for non-visual users of complex diagrams.
- Addresses limitations of existing assistive interfaces reliant on machine-readable formats.
- Utilizes a large synthetic dataset for robust model training and evaluation.
- Promotes better understanding of mathematical graphs and flowcharts.
Paper Resources
📖 Reader Mode
~2 min readAbstract:In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts. Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic dataset of node-link diagrams, reaching per-pixel accuracy over 93\%.
| Comments: | 8 pages, 6 figures, 1 table. In Proceedings of the 23rd Conference on Robots and Vision (2026) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.11320 [cs.CV] |
| (or arXiv:2606.11320v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.11320 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Michael Cormier [view email]
[v1]
Tue, 9 Jun 2026 18:02:12 UTC (864 KB)
— Originally published at arxiv.org
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